In addition, PETER is flexible to incorporate item features that can help to guide its generation.
This can be very useful when, for instance, a user proactively asks the system to explain certain feature(s) of a recommendation [1]}, e.g., price.
Then, we would expect the model to generate a targeted explanation, such as “great jacket, especially for the price”.
PETER is a small unpretrained Transformer with only 2 layers, yet it outperforms a fine-tuned BERT [2]} on most metrics by a large margin, and takes less time to train, as shown in our experiments.
This manifests the superiority of our model.
